Multi-Objective optimization strategy for multipolar magnesium electrolysis cell Based on thermal-electric model

In the operation of industrial multipolar magnesium electrolysis cell, cell voltage and current density are important indicators of the production performance of the cell, while thermal balance is a key factor for normal operation. This paper utilizes the finite element method to establish a three-dimensional full cell thermal-electric model, studying the thermal field distribution and current density distribution in a 165kA multipolar magnesium electrolysis cell. Based on the heat balance of the multipolar cell, the influence of structural parameters on current intensity, average current density at the anode surface and resistance voltage is assessed. Dimensionless analysis yielded optimization criteria, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for multi-objective optimization problems. When stringent optimization is executed, significant performance improvements can be achieved in terms of the electrolysis cell's resistance voltage and current density. Moreover, under the optimization criteria, the relative error of the 265kA multipolar magnesium electrolysis cell model is less than 1 %. The results indicate that the workflow combining the finite element method with multi-objective optimization algorithms can be applied to the optimization and scale-up design of high-current multipolar magnesium electrolysis cell.